In this paper, we compare three nested loops parallelization algorithms (Allen and Kennedy's algorithm, Wolf and Lam's algorithm and Darte and Vivien's algorithm) that use different representations of distance vectors as input. We study the optimality of each with respect to the dependence analysis it uses. We propose well-chosen examples that illustrate the power and limitations of the three algorithms. This study identifies which algorithm is the most suitable for a given representation of distance vectors.